Top AI Engineer Jobs

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models for complex business and national security mission requirements. Integrates data pipelines, evaluates model performance, applies statistical analysis, ensures data and output quality, and transforms prototypes into scalable production solutions. Provides system integration oversight and may lead distributed teams. Requires advanced education or equivalent experience and an active TS/SCI clearance with polygraph.

Designs and evaluates data architectures, ETL pipelines, storage strategies, and governance practices supporting Marine Corps AI capabilities. Advises leadership and vendors on data maturity, security, rights, interoperability, and deployment across cloud, on-premises, and tactical edge environments. Establishes data quality standards, analyzes data flows, supports experiments and wargames, and documents technical assessments. Requires substantial data engineering experience, U.S. citizenship, and TS/SCI eligibility.

Build and deploy AI-powered workflows, agents, copilots, and automations across revenue, marketing, and customer success. Integrate CRM, BI, communication, and data systems; enable natural-language analytics; establish guardrails, monitoring, documentation, and feedback loops; and measure adoption, productivity, revenue impact, and ROI. The role requires hands-on LLM, API, coding, automation, and GTM systems experience, plus cross-functional project leadership and end-to-end ownership.

Designs, builds, deploys, and optimizes generative AI and machine learning solutions on AWS, including Amazon Bedrock, foundation models, and RAG architectures. Develops document-processing automation, APIs, AI/ML models, monitoring, evaluation, and continuous-improvement processes. Integrates OCR, ICR, OMR, NLP, and data extraction capabilities while collaborating with cross-functional teams. The role also supports scalable cloud architecture, serverless applications, CI/CD, DevSecOps, and post-implementation troubleshooting.

Build and operate production AI agents that automate multi-step workflows across internal teams. Responsibilities include integrating agents with internal systems and third-party tools, deploying on Amazon Bedrock AgentCore, creating evaluation and observability frameworks, implementing guardrails, measuring impact, and establishing reusable agent-development patterns. The role also requires partnering with nontechnical teams, translating ambiguous workflows into reliable automation, documenting solutions, and driving adoption.

Design, build, and scale production-grade autonomous AI agents for Felix’s WhatsApp platform and internal operations. Responsibilities include agent architecture, LLMOps observability, product integration, deployment, evaluation pipelines, RAG optimization, cost and performance engineering, and mentoring engineers. The role requires developing multi-agent systems capable of reasoning, planning, and executing complex tasks across payments, loans, and fraud.

Integrate AI capabilities into production systems by connecting models, inference endpoints, and orchestration components. Implement reliable, secure inference flows, handle latency/token limits and batching, write integration tests, monitor and refine AI integrations, and collaborate with cross-functional teams to deliver scalable AI functionality.

Designs reference architectures for AI infrastructure networks, including L2/L3 fabrics, BGP, EVPN/VXLAN, RoCEv2, and QoS. Builds and operates labs to qualify switches, NICs, and network operating systems; conducts interoperability and performance testing; reproduces customer issues; and diagnoses congestion, packet loss, MTU, latency, PFC, and ECN problems. Collaborates with vendors and internal engineering teams to convert findings into validated configurations, automation, documentation, and product improvements.

Designs reference architectures for AI and data center networks, including BGP, EVPN/VXLAN, RoCEv2, and QoS. Builds and operates labs to qualify switches, NICs, and network operating systems; tests interoperability and performance; reproduces customer issues; and diagnoses routing, congestion, packet loss, MTU, and latency problems across GPU, fabric, and storage paths. Documents best practices, develops automation, collaborates with vendors and internal teams, and feeds findings into product development.

Lead the architecture and long-term roadmap for Relativity’s machine learning platform. Design scalable training, inference, monitoring, and ML operations systems; evaluate emerging technologies; improve model performance and cost efficiency through optimization techniques; and ensure secure, reliable AI services. Partner with product, program, and data science leaders, guide cross-functional initiatives, establish engineering standards, and mentor senior engineers across multiple teams.

Lead the architecture, development, and scaling of data pipelines and infrastructure for a customer-facing Data Lake platform. Build ETL and ingestion systems using GCP, Debezium, Pub/Sub, Dataflow, BigQuery, Python, Golang, and SQL. Establish engineering standards, deliver strategic projects, mentor engineers, influence cross-functional technical decisions, optimize data services, and shape the platform roadmap.

Designs, develops, tests, deploys, and maintains AI-powered cybersecurity capabilities, including threat detection, automated response, compliance monitoring, risk assessment, and SIEM enhancements. Develops and optimizes machine learning models, CI/CD pipelines, data workflows, and model monitoring processes. Supports AI governance, NIST and federal compliance, risk assessments, technical documentation, secure tool integration, and collaboration with SOC, engineering, and government stakeholders.

Designs, implements, and sustains AWS cloud platforms, Kubernetes environments, HashiCorp automation, CI/CD pipelines, observability, and secure deployment practices. Translates stakeholder requirements into maintainable platform capabilities, troubleshoots complex infrastructure and application issues, supports Agile planning and production transitions, and develops reusable automation, documentation, testing evidence, and operational runbooks for government digital services.

The AI and Automation Solutions Developer architects, builds, tests, deploys, and maintains production-ready AI applications, agents, integrations, and automated workflows. The role develops Power Platform solutions, APIs, enterprise integrations, and internal applications; implements security, privacy, governance, monitoring, and documentation; evaluates emerging AI technologies; measures operational impact; and partners with data, clinical, IT, quality, operations, and vendor teams to deliver scalable healthcare solutions.

Build and operate AI automation infrastructure, including n8n workflows, MCP servers, LLM integrations, agentic workflows, chat and voice agents, and AI-assisted engineering pipelines. Integrate CRM, ERP, support, fulfillment, and cloud data systems while maintaining security, measuring cost and performance, evaluating emerging tools, and presenting solutions to leadership and cross-functional teams.

Develop and maintain backend features for Guidewire’s application platform and AI services. Build secure, scalable web services using AI coding tools, collaborate on platform operations, write tested code, participate in code reviews, and contribute to AI, cloud, and data platform adoption. Work with LLMs, RAG, vector databases, AI agents, MCP, Kubernetes, and infrastructure as code while supporting continuous improvement and developer experience.

Leads full-stack software engineering within JPMorgan Chase’s supply chain technology team. Responsibilities include designing, developing, testing, debugging, and maintaining secure, scalable production systems; advancing AI-assisted engineering practices; automating recurring issue remediation; ensuring application resiliency and security; and evaluating vendor and internal technology solutions. Requires strong software development, cloud-native, agile, financial-services technology, and responsible AI expertise.

Build and deploy dependable AI agents, automations, controls, and external-facing solutions across finance, credit risk, servicing, collections, and operations. Partner with business users to scope workflows, design systems, write evaluations, instrument production processes, measure business impact, and drive adoption. Develop reusable agent patterns and harness standards while applying governance, human review, monitoring, and escalation controls.

Designs and supports AWS cloud platform products, automation tools, CI/CD pipelines, DevOps tooling, infrastructure-as-code solutions, and account provisioning automation. Leads complex technical initiatives, improves development and deployment processes, establishes security and access controls, supports operational excellence, and advises stakeholders on platform strategy. Serves as a technical subject matter expert and mentors engineers while ensuring reliable, compliant, and scalable cloud infrastructure.

Design and operate secure enterprise access to approved AI tools and model endpoints through web gateways, proxies, CASB, API gateways, and AI security controls. Implement routing, authentication, authorization, DLP, data inspection, policy enforcement, monitoring, and performance optimization for AI traffic. Partner with security, infrastructure, legal, risk, and application teams to onboard AI use cases, troubleshoot access and latency issues, develop reusable architectures, and support governance, auditability, and regulatory compliance.

Lead and grow an AI engineering organization to design, build, and operate production-grade AI services. Define architecture, drive execution, implement RAG, agents, and orchestration, ensure reliability, observability, and CI/CD, and partner with Product, Data, Security, and business stakeholders while recruiting and mentoring engineers.

Lead an AI platform effort to build production AI capabilities that convert building, project, asset, and operational data into reusable, governed solutions. Design and deliver RAG, agents, multimodal data pipelines, orchestration, evaluation, and human-in-the-loop workflows from prototype to production, partnering with business, security, and operations.

Build Trustible’s agentic AI infrastructure, including MCP tool integrations, AI agent data pipelines, and reliable backend platform features in Python and Django. Design scalable ingestion and processing systems, make architecture decisions, harden production systems, and establish engineering standards. Partner with the CTO on the agentic roadmap while mentoring engineers and balancing rapid delivery with long-term maintainability.

Designs, builds, and operates scalable enterprise data and AI platforms using Microsoft Fabric and Azure. Develops cloud-native data solutions, AI-enabled applications, automation workflows, and CI/CD pipelines. Applies Python, SQL, Terraform, Jenkins, GitHub, testing, and modern software engineering practices. Supports production platforms, improves reliability and performance, and collaborates with data engineers, architects, scientists, and product teams on secure, governed, enterprise-scale data capabilities.

Provides professional support for risk technology and AI projects, completing deliverables, collaborating with stakeholders, following quality standards, and adapting to changing business needs. The role requires a related bachelor’s degree or equivalent, 5–8 years of relevant experience, and supervisory experience. Travel to client or organizational locations may be required, with potential increased hours during peak business cycles.